Using data collected by a personal electronic device to identify a vehicle

ABSTRACT

Apparatus and methods are disclosed for identifying a vehicle using data collected by a personal electronic device residing in the vehicle during its operation. By enabling the vehicle to be identified using data collected by the personal electronic device, embodiments of the invention may allow other information captured by the personal electronic device relating to the user&#39;s operation of the vehicle to be associated with the vehicle.

RELATED APPLICATION

This application is a continuation of, and claims priority under 35U.S.C. § 120 to, commonly assigned U.S. patent application Ser. No.15/585,013, filed May 2, 2017, entitled “Using Data Collected By APersonal Electronic Device To Identify A Vehicle,” bearing, the entiretyof which is incorporated herein by reference.

BACKGROUND

Conventionally, direct measurements of the behavior of a vehicleoperator during vehicle operation are made using dedicated components(i.e., by components which are designed to be permanently associatedwith the vehicle). For example, some conventional hardware componentsare embedded within a vehicle (e.g., during manufacture) and other,so-called “aftermarket” components are designed for permanentinstallation within a vehicle.

SUMMARY

The Assignee has appreciated that using a personal electronic device(e.g., a smartphone, tablet, wearable device, gaming console, and/or anyother suitable electronic device(s) associated with a user) to collectdata relating to the behavior of a vehicle operator during operation mayreduce the cost of collecting such data, since it would reduce oreliminate the need for dedicated hardware to do so. The Assignee hasalso appreciated, however, that one issue relating to using a personalelectronic device to collect such data is that the data is attributableto the user of the personal electronic device, rather than to anyparticular vehicle operated by the user, and that the user may travel inany number of different vehicles over time. Some embodiments of theinvention, then, are directed to identifying a vehicle in which a usertravels using data collected by a personal electronic device.Identifying the vehicle using data collected by a personal electronicdevice may allow other information captured by the personal electronicdevice which relates to the user's operation of the vehicle to beassociated with the vehicle. As a result, costly dedicated hardware neednot be used to collect direct measurements of the user's behavior duringvehicle operation.

The Assignee has further appreciated that being able to identify avehicle using data collected by a personal electronic device may enableinsurers to offer usage-based insurance (UBI) at lower cost than ifcostly dedicated hardware were used. Specifically, an insurer may tailorthe underwriting of a UBI policy to a specific vehicle based on directmeasurements of an individual's behavior in operating the vehicle,without needing to employ costly hardware to collect such measurements.However, it should be appreciated that the invention is not limited tobeing used to collect data for underwriting a UBI policy, as the abilityto identify a vehicle using data collected by a personal electronicdevice may have any of numerous other uses.

In some embodiments, the accelerometer, gyroscope and/or globalpositioning system (GPS) components of a personal electronic device mayprovide information that is useful for identifying a vehicle. Forexample, in some embodiments of the invention, readings produced by theaccelerometer of the personal electronic device may be sampled at some(e.g., predefined) frequency during vehicle operation, and patternspresent within these readings may be used to identify the vehicle. Insome embodiments, the readings may indicate acceleration in the x-, y-and z-directions (or in any other suitable direction(s), as defined inany suitable manner) in the time domain, and the readings may bedecomposed into components represented in the frequency domain. In thisrespect, the Assignee has appreciated that the vibration patternsexpressed by a vehicle over a range of frequencies may uniquely identifythe vehicle. As a result, acceleration data captured by a user'spersonal electronic device while traveling in a vehicle may be processedto identify the vehicle.

In some embodiments, the acceleration data which is used to identify avehicle is captured while the vehicle operates in a known, predeterminedmanner. As one example, the Assignee has appreciated that (for reasonsdescribed in further detail below) the computational expense associatedwith identifying a vehicle based upon its vibration patterns may belowest when the vehicle is idling. As a result, in some embodiments, theGPS unit of a personal electronic device may be used to identify periodswhen the location of the personal electronic device, and thus thevehicle, is unchanged (as this may indicate when the vehicle is idling),and accelerometer readings captured during these periods may be analyzedto identify the vehicle.

In some embodiments, one or more mathematical models and/or otheralgorithms may be used to process readings from a personal electronicdevice and/or transformed representations thereof to identify a vehicle.For example, one or more mathematical models may be used to identify thecharacteristics of a vehicle's vibration patterns which allow thevehicle to be distinguished from other vehicles. The model(s) may, forexample, be initially trained using data gathered from any suitablenumber of vehicles, over any suitable period of time, and may be updatedover time as more and more data (e.g., on more and more vehicles overtime) is gathered and processed. As a result, the accuracy with whichthe model(s) identify a vehicle may increase over time.

A vehicle may be identified using its vibration patterns at any suitablejuncture(s) and by any suitable component(s). For example, in someembodiments, readings collected from the components of a personalelectronic device traveling in a vehicle may be processed by one or tomore modules executed by a server which is physically remote from thepersonal electronic device, at some time after the readings arecaptured. By contrast, in some embodiments these readings may beprocessed in real-time by the personal electronic device itself as datais captured, such as to provide real-time feedback to the user of thedevice and/or to enable functionality associated with a particularvehicle or type of vehicle provided by the device. Any of numerousdifferent modes of implementation may be employed.

Some embodiments of the invention are directed to a method, comprisingacts of: (A) receiving data captured by a personal electronic deviceresiding within a vehicle during operation of the vehicle: and (B)identifying the vehicle based at least in part on the data.

Other embodiments of the invention are directed to least onecomputer-readable storage medium having instructions recorded thereonwhich, when executed by a computer, cause the computer to perform amethod comprising acts of: (A) receiving data captured by a personalelectronic device residing within a vehicle during operation of thevehicle: and (B) identifying the vehicle based at least in part on thedata.

Still other embodiments of the invention are directed to a computingsystem, comprising: at least one computer-readable storage device,storing instructions; and at least one computer processor, programmedvia the instructions to: receive data captured by a personal electronicdevice residing within a vehicle during operation of the vehicle: andidentify the vehicle based at least in part on the data.

The foregoing is a non-limiting summary of certain aspects of theinvention. Some embodiments of the invention are described in furtherdetail in the sections that follow.

BRIEF DESCRIPTION OF DRAWINGS

Various aspects and embodiments of the invention are described belowwith reference to the following figures. It should be appreciated thatthe figures are not necessarily drawn to scale. Items appearing inmultiple figures are indicated by like reference numerals in each figurein which the items appear.

FIG. 1 is a block diagram depicting a representative system foridentifying a vehicle based on information captured using a personalelectronic device, in accordance with some embodiments of the invention;

FIG. 2 is a flow chart depicting a representative process foridentifying a vehicle using information captured using a personalelectronic device, in accordance with some embodiments of the invention;

FIG. 3 is a flow chart depicting a representative process for capturingand processing acceleration data captured by a personal electronicdevice to identify a vehicle, in accordance with some embodiments of theinvention;

FIG. 4 depicts power spectra representing acceleration data captured bya personal electronic device traveling in two different vehicles, inaccordance with some embodiments of the invention;

FIG. 5 is a flow chart depicting a representative process for processingtrip data captured by a personal electronic device, in accordance withsome embodiments of the invention;

FIGS. 6A-6B depict representations of acceleration data captured duringtrips in two different vehicles, in accordance with some embodiments ofthe invention;

FIG. 7 is a flow chart depicting a representative process for generatingoutput for display based upon a vehicle being identified, in accordancewith some embodiments of the invention; and

FIG. 8 is a block diagram depicting a representative computing systemwhich may be used to implement certain aspects of the invention.

DETAILED DESCRIPTION

Some embodiments of the invention are directed to identifying a vehicleusing data collected by a personal electronic device. By enabling thevehicle to be identified using data collected by the personal electronicdevice, some embodiments of the invention may allow other informationcaptured by the personal electronic device relating to the user'soperation of the vehicle to be associated with the vehicle, so thatcostly hardware which is permanently associated with the vehicle is notrequired.

It should be appreciated that, as used herein, the phrase “identifying avehicle” need not necessarily mean uniquely identifying a particularvehicle, such as by distinguishing one 2007 Toyota Corolla from another2007 Toyota Corolla (although some embodiments of the invention mayenable vehicle identification at such a granular level). Rather, in someembodiments, “identifying a vehicle” may mean identifying the year,make, model, class and/or type of the vehicle, or some subset of thesevehicle characteristics, and in some embodiments, identifying a vehiclemay mean distinguishing between one of a set of vehicles and another ofthe set without determining any of the aforementioned vehiclecharacteristics. For example, identifying a vehicle may mean being ableto discriminate between a Toyota Corolla and a BMW M3, between a 2010BMW M3 and a 2012 BMW M3, between a BMW M3 and a BMW 325i, between asedan and a sport utility vehicle, and/or between an eighteen-wheelertruck and a vehicle which is not an eighteen-wheeler truck. It may also,or alternatively, mean being able to discriminate one year, make, model,class and/or type of vehicle from all others, such as being able todetermine whether a vehicle is a 2007 Toyota Corolla or not. It mayalso, or alternatively, mean being able to discriminate between one of aset of vehicles in which a user is known to travel (e.g., one vehiclethat the user has insured) and another of the set (e.g., another vehiclethat he/she has insured). Identification of a vehicle may take any ofnumerous forms, and the invention is not limited to identifying avehicle in any particular way.

In some embodiments, the accelerometer, gyroscope and/or GPS componentsof a personal electronic device may provide information that is usableto identify a vehicle. For example, in some embodiments, accelerometerreadings indicating acceleration of the personal electronic device inone or more directions (e.g., in x-, y- and z-directions, and/or in anyother suitable direction(s), as defined in any suitable way(s)) overtime may be sampled while the personal electronic device resides in thevehicle during its operation, and these readings may be transformed(e.g., via a Fourier transform, and/or any other suitable technique(s))to produce representations in the frequency domain. Theserepresentations may reveal oscillations within or across one or morefrequency ranges, akin to a vibrational “signature” that may be usefulin identifying the vehicle. Gyroscope and/or GPS readings may, forexample, be used to determine when the vehicle and/or personalelectronic device are being operated in predefined ways which theAssignee has recognized yield readings which are most useful inidentifying the vehicle. For example, gyroscope and/or other instrumentreadings may be used to identify periods during which the personalelectronic device is in (and/or is not in) one of a set of predeterminedorientations and/or modes of use. For example, the Assignee hasappreciated that accelerometer readings taken while the personalelectronic device is in active use may not be as useful in identifyingthe vehicle, and so accelerometer readings taken during periods ofactive use (e.g., as indicated by readings from a gyroscope and/or otherinstruments) may be ignored. As another example, GPS readings may beused to identify periods when the location (e.g., geolocation) of thepersonal electronic device, and thus the vehicle, is unchanged (i.e.,the vehicle is idling), as the Assignee has appreciated that thecomputational expense associated with identifying a vehicle based uponacceleration data is lowest when the vehicle is idling.

FIG. 1 depicts a representative system 100 for collecting and processingdata from a personal electronic device to identify a vehicle in whichthe device travels. In representative system 100, personal electronicdevice 110 travels within vehicle 101, but is not permanently associatedwith vehicle 101, or any other particular vehicle, and may betransported by a user from one vehicle to another. As such, vehicle 101is represented using dotted lines in FIG. 1. Although a label in thesingular person is used herein to reference a personal electronicdevice, it should be appreciated that the components used to collectdata useful for identifying a vehicle may be physically and/or logicallydistributed across any suitable number of hardware devices, each adaptedfor transport by a user between settings (e.g., vehicles) as the userdesires.

Vehicle 101 may be any suitable vehicle, adapted for travel on land, seaand/or air. For example, vehicle 101 may be an automobile, truck,motorcycle, boat, helicopter, airplane, and/or any other suitable typeof vehicle.

In representative system 100, personal electronic device 110 comprisesinertial measurement unit (IMU) 102, processing logic 104, GPS unit 106,and transceiver 108. IMU 102 may comprise any suitable collection ofcomponents for capturing the movement and/or orientation of personalelectronic device 110. For example, in some embodiments, IMU 102 maycomprise one or more accelerometers, gyroscopes, magnetometers, and/orany other suitable components. IMU 102 provides data to processing logic104 for processing. This processing may take any of numerous forms, andsome representative processing modes are described in further detailbelow. For example, in some embodiments, processing logic 104 maycomprise software code that is executed to apply one or moremathematical models to readings captured by IMU 102 so as to identify avehicle. GPS unit 106 captures information relating to the location ofpersonal electronic device 110 over time, which may be used to infer thespeed at which personal electronic device 110 (and thus the vehicle inwhich it travels) is moving at any given time. It should be appreciated,however, that vehicle location and/or speed may be measured in any ofnumerous ways, and that embodiments of the invention are not limited tousing a GPS unit to measure device location and/or speed. Any suitabletechnique(s) and/or component(s) may be employed.

Personal electronic device 110 further includes transceiver 108 whichallows personal electronic device 110 to communicate via network(s) 120with server 130. Network(s) 120 may comprise any suitable communicationsinfrastructure, and employ any suitable communication protocol(s), asthe invention is not limited in this respect. For example, if personalelectronic device 110 comprises a smartphone adapted for cellularcommunication, then network(s) 120 may comprise one or more cellularnetworks.

Server 130 comprises communication facility 132 for receivingtransmissions from, and sending transmissions to, personal electronicdevice 110 via network(s) 120. Communication facility 132 may take anyof numerous forms, which may be driven by the communicationsinfrastructure comprising network(s) 120. For example, if network(s) 120comprise one or more wired networks, then communication facility 132 maycomprise a network adapter useful for receiving transmissions over thewired network(s), and if network(s) 120 comprise one or more wirelessnetworks, then communication facility 132 may comprise a radio usefulfor receiving transmissions over the wireless network(s). Of course,communication facility may comprise components useful for receivingtransmissions over different types of networks.

Server 130 includes processing logic 134. In some embodiments,processing logic 134 may comprise software code that is executable toprocess information received from personal electronic device 110 vianetwork(s) 120. As an example, processing logic 134 may be used toprocess acceleration data (and/or representations thereof) received frompersonal electronic device 110 to identify a vehicle in which personalelectronic device 110 has traveled. Processing logic 134 storesinformation in, and retrieves information from, data repository 140. Forexample, processing logic 134 may cause acceleration data and/orrepresentations thereof received from personal electronic device 110 tobe stored in data repository 140. Results generated by processing thedata received from personal electronic device 110 may be stored in datarepository 140. Although only one data repository 140 is shown in FIG.1, it should be appreciated that any suitable number of datarepositories may be employed.

Results generated by processing logic 134 may be provided to userfeedback generator 136. In some embodiments, user feedback generator 136may comprise software code which is executable to generate informationfor presentation to a user (e.g., to a user of personal electronicdevice 110, such as to provide real-time feedback, or to one or moreother users not represented in FIG. 1, as described in further detailbelow). If the information generated by user feedback generator 136 isto be provided to a user of electronic device 110, it may be transmittedto personal electronic device 110 via communication facility 132 andnetwork(s) 120.

It should be appreciated that although the description above includesreferences to several components of representative system 100 beingimplemented at least in part via software, any of the components ofrepresentative system 100 may be implemented using any suitablecombination of hardware and/or software components. As such, eachcomponent should be generically considered a controller which may employany suitable collection of hardware and/or software components toperform the described function.

It should also be appreciated that although only a single server 130 andsingle personal electronic device 110 is shown in FIG. 1, any suitablenumber of server components may be used to process information receivedfrom any suitable number of personal electronic devices. Any informationgathered by a personal electronic device may be processed by componentswhich are logically and/or physically distributed across any suitablenumber of server devices. In similar fashion, any processing of suchinformation may be logically and/or physically distributed acrossprocessing logic 104 on a personal electronic device 110 and processinglogic 134 on a server 130, in any suitable fashion. Any of numerousdifferent modes of implementation may be employed.

FIG. 2 depicts a representative high-level process 200 for identifying avehicle based upon data which is captured by a personal electronicdevice 110. In summary, representative process 200 involves training oneor more models to identify a vehicle using data captured by a personalelectronic device in the act 210, subsequently receiving vehicleoperation data in the act 220, and applying the model(s) developed inthe act 210 to identify the vehicle in the act 230. In some embodiments,representative process 200 may optionally (as indicated by the dottedlines in FIG. 2) include employing the vehicle identification to supplyvehicle-specific information to a user in the act 240, receiving othervehicle operation data in the act 250, and/or revising the model(s)trained in the act 210 based upon vehicle operation data collected inthe act 250. The acts comprising representative process 200 aredescribed in further detail in the paragraphs that follow.

At the start of representative process 200, one or more mathematicalmodels are trained to identify a vehicle based upon data captured by oneor more personal electronic devices traveling in the vehicle in the act210. A representative process 300 for training one or more models toidentify a vehicle using data captured by one or more personalelectronic devices is shown in more detail in FIG. 3.

Representative process 300 starts in act 310, wherein acceleration datais collected by one or more personal electronic devices while eachdevice travels in one or more vehicles. For example, an accelerometercomponent of each personal electronic device may capture accelerationcomponents in three dimensions, referred to herein as x-, y- andz-directions for simplicity. Of course, acceleration may becharacterized as occurring in any suitable direction(s), which may ormay not correspond to x-, y- and/or z-directions in a Cartesiancoordinate system. The invention is not limited in this respect.

In some embodiments, for purposes of training the model(s), theacceleration data collected in the act 310 may be specificallyassociated with a particular vehicle, set of vehicles, and/or vehicle(s)exhibiting certain qualities. For example, one acceleration data setcaptured by a personal electronic device in vehicle A may be labeled asrelating to vehicle A, another acceleration data set captured in vehicleB may be labeled as relating to vehicle B, and/or another accelerationdata set captured in vehicle C may be labeled as relating to vehicleshaving one or more qualities exhibited by vehicle C.

As noted above, in some embodiments, acceleration data may be capturedby each personal electronic device while it is in a vehicle exhibitingone or more predetermined characteristics or conditions. For example, insome embodiments, acceleration data is captured by each personalelectronic device as it is in a vehicle in operation (e.g., its engineis running) but not moving—i.e., while it is idling. In this respect,the Assignee has recognized that capturing acceleration data while avehicle is idling may offer several advantages over capturing such datawhile the vehicle is in motion. One advantage flows from the Assignee'sobservation that the vibration patterns exhibited by a moving vehiclecommonly vary based upon operating conditions such as weather, roadconditions, the vehicle's speed, etc., while vibration patternsexhibited by an idling vehicle are relatively consistent. The Assigneefurther recognized that because any mathematical model(s) used toidentify a vehicle using acceleration data would have to compensate forthe variances caused by operating conditions (i.e., by performingadditional computations), it is computationally less expensive toidentify a vehicle using acceleration data captured while the vehicle isidling than using data captured while the vehicle is in motion.

Another advantage flows from the ability to use a wide range ofconventional personal electronic devices to capture acceleration data.In this respect, the Assignee has observed that many personal electronicdevices have the ability to sample acceleration data at a maximumfrequency of 100 Hz (i.e. every 10 milliseconds). The Assignee has alsorecognized that, due to known phenomena, sampling acceleration data at100 Hz provides the ability to reliably measure vibration patterns atfrequencies up to 50 Hz. The Assignee has further recognized that manyvehicles produce vibrations in frequency ranges between 10 Hz and 30 Hzwhile idling, but that vehicles commonly produce vibrations atfrequencies greater than 50 Hz when moving. Thus, identifying a vehicleusing acceleration data captured while a vehicle is idling enables awide range of conventional personal electronic devices to be used tocapture the data.

An additional advantage flows from the ability to sample at a relativelylow frequency (e.g., at 100 Hz). Specifically, sampling less frequentlyproduces less data to be processed in identifying the vehicle, therebyreducing the computational expense of doing so.

It should be appreciated, however, that the invention is not limited tocapturing acceleration data captured while a vehicle is idling, or toanalyzing acceleration data within any specific range of frequencies toidentify a vehicle. Any suitable technique(s) may be employed, and thetechnique(s) may vary over time. For example, over time, if personalelectronic devices become capable of sampling acceleration data atfrequencies which exceed 100 Hz, and/or the computational expenseassociated with identifying a vehicle using acceleration data capturedwhile the vehicle is in motion becomes less of a concern, then a vehiclemay be identified using data captured while the vehicle is moving.

Referring again to FIG. 3, at the completion of act 310, representativeprocess 300 proceeds to act 320, wherein acceleration data observedduring one or more specific time windows is extracted. Extraction ofdata observed during one or more windows may be performed in anysuitable way. For example, in some embodiments, a subset of theacceleration readings taken in the act 310 may be discarded, such as toaccount for initial setup and/or tear-down of the personal electronicdevice in the vehicle. In some embodiments, any non-discarded data maybe divided into information captured during five second windows, with a2.5 second overlap between windows. In this respect, the Assignee hasrecognized that the longer the sampling window, the greater theresolution of the resulting representation of the sampled data in thefrequency domain, which should enable easier identification ofvibrational patterns which distinguish one vehicle from another. TheAssignee has also observed that sampling windows shorter than fiveseconds commonly produce representations in the frequency domain inwhich such distinguishing features may be harder to identify, and thatsampling windows longer than five seconds commonly producerepresentations having resolutions not appreciably greater than thoseproduced from data in five second sampling windows. Of course, anysuitable sampling window and/or overlap between windows may be used.Moreover, the sampling window and overlap between windows may be fixedor variable. The invention is not limited in this respect.

In representative process 300, act 320 also involves interpolating datain each sampling window on to a regularly sampled time grid. This may beaccomplished in any suitable fashion, such as via linear interpolation.The result of the act 320 is data describing acceleration in the x, yand z directions, captured in the time domain, such as is shown in thegraphs shown at the top of each of FIGS. 6A and 6B. These graphs aredescribed in further detail below.

At the completion of act 320, representative process 300 proceeds to act330, wherein the acceleration data in the time domain is used to computepower spectra in the frequency domain for each acceleration component.This may be performed using any suitable technique(s). For example, insome embodiments, a Fourier transform may be used to compute powerspectra over a frequency range for each acceleration component in thetime domain. FIG. 4 depicts representative power spectra computed foracceleration components in the x, y and z directions for two differentvehicles, labeled 410 and 420. Specifically, a spectral plot showingvibration patterns for one vehicle in the x-direction across a range offrequencies from 2 Hz to 50 Hz is shown at 412, a spectral plot showingvibration patterns for the same vehicle in the y-direction across thesame frequency range is shown at 414, and a spectral plot showingvibration patterns for the same vehicle in the z-direction is shown at416. Corresponding spectral plots showing vibration patterns in the x-,y-, and z-directions for another vehicle across the same range offrequencies are shown at 422, 424 and 426. Of course, the spectral plotsin FIG. 4 are merely representative, and other representations in thefrequency domain may vary from those shown in FIG. 4 in any of numerousways. Any suitable manner of measuring and/or representing spectralpower in the frequency domain may alternatively, or additionally, beused, as the invention is not limited in this respect.

It should be appreciated that the invention is not limited to performinga transformation of acceleration data in the time domain to computepower spectra in the frequency domain prior to providing input to themodel(s) used in identifying a vehicle. For example, in someembodiments, the model(s) may take the acceleration data extracted inthe act 320 directly as input, so that no transformation is performedprior to the model(s) receiving the data. In other embodiments, themodel(s) may be used to transform acceleration data. For example, insome embodiments, one or more models may discern the optimal manner oftransforming acceleration data (e.g., in a manner which may not involvea Fourier transformation) from the data itself. Any suitable mode(s) ofimplementation, which may or may not involve transforming accelerationdata prior to providing it to the model(s) used in identifying avehicle, may be used.

The Assignee has recognized that, in certain implementations,acceleration in the x-, y- and/or z-directions may not be isotropic, sothat the spectral plots shown in FIG. 4 may represent vibrations whichare different than if the personal electronic device had been orienteddifferently at the time acceleration data was captured. As such, in someembodiments, synthetic re-orientation of the personal electronic devicemay be applied (e.g., via calculation). A determination whethersynthetic re-orientation of the device is to be performed may be madebefore or after power spectra are calculated in the act 330 (i.e., ifsuch calculation is performed). For example, a visual inspection of thespectrograms shown in FIG. 4 may reveal that vibration in one or moredirections is not isotropic, and so synthetic re-orientation of thedevice may be performed so that the spectrograms may be re-generated toaccount for the synthetically reoriented data. If syntheticre-orientation is performed, it may be done in any suitable fashion, asthe invention is not limited in this respect.

Referring again to FIG. 4, it can be seen from the representativespectral plots at 410 and 420 that the two vehicles represented exhibitspecific vibrational patterns in certain frequency ranges. For example,it can be seen that the vehicle represented at 410 exhibits significantacceleration in the x-, y- and z-directions at about 17-19 Hz and 35-37Hz. It can also be seen that the vehicle represented at 420 exhibitssignificant acceleration in in the x-, y- and z-directions at about atabout 18-20 Hz, 37-39 Hz, and 22-24 Hz. That is, each vehicle exhibits avibrational “signature” in all three directions that may be used todistinguish the vehicle from another. Of course, the vibrational“signature” which makes one vehicle distinguishable from another neednot involve acceleration in all three directions, as any suitableacceleration pattern may be used to distinguish one vehicle fromanother. Indeed, a vibrational signature useful for distinguishing onevehicle from another may comprise a complex pattern of excitation withinnumerous frequency ranges.

Referring again to FIG. 3, at the completion of act 330, representativeprocess 300 proceeds to act 340, wherein the power spectra data computedin the act 330 is prepared for input to one or more mathematical modelsby being “flattened” into a one-dimensional array. For example, datarepresenting power spectra for x-, y- and z-direction accelerationcomponents may be organized into an array wherein each element includesspectral power data for only one of the x-, y- or z-directions.

Representative process 300 then proceeds to act 350, wherein someportion of the data produced in the act 340 is provided to one or moremathematical models for training of the model(s). The portion that isprovided to the model(s) for training may be any suitable portion, andmay, for example, depend on the extent to which the model(s) have beentrained. For example, if model training is at the beginning stageswherein overall model performance is being evaluated and parameters arebeing tuned, then something less than the entirety of the data producedin the act 340 may be provided. By contrast, if model training is nearlycomplete, then the entirety of the data produced in the act 340, or anapproximation thereof, may be provided to the model(s).

Any suitable type(s) of mathematical model may be trained to distinguishone vehicle from another using the data provided in the act 350. Forexample, a machine learning algorithm (e.g., a neural network) may betrained to use this data to learn how to distinguish one vehicle fromanother based on the vehicles' vibration patterns. For example, amachine learning algorithm may learn how to identify the repeatedstructural features within power spectra data which uniquely identify aparticular vehicle (and/or expressed within acceleration and/or otherdata, if transformation into power spectra data is not performed). Ofcourse, a machine learning algorithm need not be used, as any suitablemathematical model(s) and/or computational approach(es) may be employed.

In some embodiments, a model may be trained to discern differences inspectral signatures from a variety of different vehicles, and toidentify the particular features exhibited within that data that arediscriminative, so that the model may become more efficient over time(e.g., as it is provided with more and more data collected from devicestraveling in more and more vehicles) at identifying vehicles. Further,once a model identifies the features which are discriminative, thesefeatures may be identified to one or more other models, which may nothave been trained on the same data. The features may constitute a“fingerprint” which the receiving model(s) may use in discriminatingamongst different vehicles. For example, a fingerprint identified by onemodel may be used by one or more other models to specifically identify aparticular vehicle, or to determine that a newly received vibrationalsignature is from a different vehicle than the one to which thefingerprint relates.

At the completion of act 350, representative process 300 then proceedsto act 360, wherein another subset of the data generated in the act 340is provided to the model(s) to which the other subset was provided inthe act 350, for test identification of one or more vehicles. Forexample, the remainder of the data generated in the act 340 may beprovided to the model(s) in this step. Alternatively, some other subsetmay be provided, as determined in any suitable manner.

Test vehicle identification in the act 360 may, in some embodiments, beperformed to determine how effective the model(s) trained in the act 350are at distinguishing between the vehicles represented in the dataprovided in the act 310, such as to determine whether the model(s) needfurther training or refinement. The effectiveness of the model(s) inidentifying a vehicle may be determined in any suitable fashion. Forexample, if the model(s) successfully identify less than a thresholdpercentage of vehicles using the data provided in the act 360, then itmay be determined that the model(s) should be further trained beforebeing used further. As such, the act 370, in which the model(s) arerevised, is shown in dotted lines in FIG. 3. If the model(s) are revisedin act 370, any suitable revision, adjustment and/or refinement may beapplied. As one example, more training data may be passed to themodel(s) to train the model(s) to identify spectral features thatconstitute a vehicle's vibration “signature,” to learn which spectralfeatures are most useful in discriminating one vehicle from another,etc. Any revision, adjustment and/or refinement may be applied in anysuitable fashion, whether using manual techniques, automatic techniques,or a combination thereof. Representative process 300 then completes.

Referring again to FIG. 2, at the completion of the act 210,representative process proceeds to the act 220, wherein vehicleoperation data that is useful for vehicle identification is received. Arepresentative process 500 for capturing the vehicle operation datauseful for identifying a vehicle is shown in greater detail in FIG. 5.

At the start of representative process 500, a body of vehicle operationdata is received in act 510. This may be performed in any of numerousways. For example, a personal electronic device may provide accelerationmeasurements captured in a vehicle during operation.

Representative process 500 then proceeds to act 520, wherein one or moresubsets of the vehicle operation data are identified as having beencaptured during periods when predetermined conditions exist, andextracted. As described above, some embodiments of the invention mayinvolve identifying a vehicle using data captured while the vehicle isidling, and so in some embodiments act 520 may involve identifying thesubsets of vehicle operation data which are captured when the vehicle isidling, and extracting these subsets.

Identifying the subsets of vehicle operation data which are capturedwhen a vehicle is idling may be performed in any suitable fashion. Inthis respect, FIGS. 6A and 6B each depict acceleration data (i.e.,acceleration components in the x-, y- and z-directions) captured by anaccelerometer of a personal electronic device over a particular timeinterval in portion 610, and vehicle speed data inferred from vehiclelocation information captured by a GPS unit of the personal electronicdevice over the same time interval in portion 620. It can be seen inportions 620A and 620B that during certain time intervals, thecorresponding vehicle is not moving (i.e., its speed is 0 m/s).Specifically, in FIG. 6A, time intervals when the corresponding vehicleis not moving are labeled 601, 602, 603, 604, 605, 606 and 607.Acceleration data during these same time intervals are labeled 611, 612,613, 614, 615, 616 and 617. Similarly, in FIG. 6B, the time intervalswhen the corresponding vehicle is not moving are labeled 621, 622, 623,624, 625, 626, 627, 628, 629, 630 and 631. The acceleration data duringthese same time intervals are labeled 641, 642, 643, 644, 645, 646, 647,648, 649, 650 and 651. Thus, for the representative vehicle operationdata represented in FIGS. 6A-6B, act 520 may involve extractingacceleration data for the time intervals labeled 611, 612, 613, 614,615, 616, and 617, or 641, 642, 643, 644, 645, 646, 647, 648, 649, 650and 651. At the completion of act 520, representative process 500completes.

Referring again to FIG. 2, at the completion of the act 220,representative process 200 proceeds to act 230, wherein the model(s)trained in the act 210 are applied to the vehicle operation datareceived in the act 220 in identifying a vehicle. For example, in someembodiments, the vehicle operation data received in the act 220 may beprocessed in a manner similar to that which is described above withreference to FIG. 3, such that the acceleration information in the timedomain comprising the vehicle operation data received in the act 220 isused to compute power spectra in the x-, y- and z-directions in thefrequency domain, and the model(s) trained in the act 210 are applied tothe power spectra data for use in identifying the vehicle in which thepersonal electronic device was traveling when the acceleration data wascaptured. Of course, some embodiments of the invention may not involveusing the model(s) trained in the act 210 in identifying a vehicle, asany of numerous other techniques may be used.

It should be appreciated that embodiments of the invention are notlimited to using only the model(s) trained in the act 210 in identifyinga vehicle. For example, as described above, discriminative informationidentified by one model may be supplied to one or more other models forsubsequent use in identifying a vehicle. Moreover, the information thatis used in identifying a vehicle is not limited to that which isproduced by a model. Any other suitable form(s) of information may alsobe used in identifying a vehicle in act 230. The invention is notlimited in this respect.

At the completion of the act 230, representative process 200 may proceedto the act 240, as indicated by the dotted lines in FIG. 2. In act 240,the vehicle identification performed in act 230 is used to provideinformation to a user. A representative process 700 for using a vehicleidentification to provide information to a user is shown in greaterdetail in FIG. 7.

At the start of representative process 700, a vehicle identification(e.g., generated in the act 230 shown in FIG. 2) is processed to produceoutput in act 710. This output may take any of numerous forms, dependingon how it is to be used. In one example, a vehicle identification may beprocessed to produce output that is designed for display to the user ofthe personal electronic device in real time. For example, someembodiments of the invention may provide for displaying vehicle-specificinformation such as warnings, maintenance reminders, readouts, and/orother information to a user while he/she travels in a vehicle. In theseembodiments, act 710 may involve producing a representation of theinformation to be displayed to the user, and act 720 may involverendering the display.

In another example, a vehicle identification may be used to manage powerconsumption by a personal electronic device. For example, if it isdetermined that the user is currently traveling in a vehicle for whichacceleration data need not be captured (e.g., because the vehicle is notof interest in the analysis described above), then the data capturefunctionality may be turned off so as to conserve power consumption bythe personal electronic device. In these embodiments, act 710 mayinvolve producing output for use by software which controls accelerationdata capture, and act 720 may providing the output to the software.

At the completion of the act 720, representative process 700 completes.

Referring again to FIG. 2, at the completion of optional act 240,representative process 200 proceeds to act 250, wherein other vehicleoperation data is received. Like act 240, act 250 is also optional, asindicated by the dotted lines in FIG. 2.

The other vehicle operation data which is received in the act 250 maytake any of numerous forms. As one example, this data may relate to anindividual's behavior in operating the vehicle, such as which may becaptured by the accelerometer, gyroscope and/or GPS unit of the personalelectronic device. Of course, the data captured in the act 250 need notbe captured by components of the personal electronic device, and may becaptured by any suitable component(s), whether internal or external tothe vehicle. In this respect, the vehicle identification performed inthe act 230 may indicate a time period during which the identifiedvehicle was being operated, and information gathered by the othercomponent(s) may be correlated to the vehicle based on the time at whichthe information was gathered describe may be used to produce informationfor other users.

One representative use for the vehicle operation data received in theact 250 is by an insurer which underwrites a UBI policy on the vehicle.However, any of numerous other uses are possible. As one example, themanager of a team of salespeople each assigned to one of a fleet ofvehicles may find data relating to each salesperson's operation ofhis/her assigned vehicle useful for training purposes. As anotherexample, behavioral data relating to one or more operators,organizations, circumstances, time periods, etc., may be aggregated foranalysis.

At the completion of optional act 250, representative process 200proceeds to act 260, wherein the model(s) applied in the act 230 arerevised. Like acts 240 and 250, act 260 is optional, as indicated by thedotted lines in FIG. 2.

The model(s) may be revised in any of numerous ways. In someembodiments, the vehicle operation data which is identified andextracted in act 520 (FIG. 5) may be used to further train the model(s),such as to identify particular spectral features which are useful fordiscriminating between one vehicle and another. As a result of suchrevision, the model(s) may become more effective at identifying vehiclesover time. At the completion of the act 250, representative process 200completes.

It should be appreciated that although the embodiments described aboverelate to identifying a vehicle using acceleration data which iscaptured by a personal electronic device (or derivations orrepresentations thereof), the invention is not limited to employinginformation which relates to acceleration, or to using information whichis captured by a personal electronic device, to identify a vehicle. Anysuitable information, captured by any suitable device(s), may be used toidentify a vehicle.

It should also be appreciated that, in some embodiments, the methodsdescribed above with reference to FIGS. 2, 3, 5 and 7 may vary, in anyof numerous ways. For example, in some embodiments, the steps of themethods described above may be performed in a different sequence thanthat which is described, a method may involve additional steps notdescribed above, and/or a method may not involve all of the stepsdescribed above.

It should further be appreciated from the foregoing description thatsome aspects of the invention may be implemented using a computingdevice. FIG. 8 depicts a general purpose computing device, in the formof a computer 810, which may be used to implement certain aspects of theinvention. For example, computer 810, or components thereof, mayconstitute the personal electronic device 110 or server 130, orcomponents thereof.

In computer 810, components include, but are not limited to, aprocessing unit 820, a system memory 830, and a system bus 821 thatcouples various system components including the system memory to theprocessing unit 820. The system bus 821 may be any of several types ofbus structures including a memory bus or memory controller, a peripheralbus, and a local bus using any of a variety of bus architectures. By wayof example, and not limitation, such architectures include IndustryStandard Architecture (ISA) bus, Micro Channel Architecture (MCA) bus,Enhanced ISA (EISA) bus, Video Electronics Standards Association (VESA)local bus, and Peripheral Component Interconnect (PCI) bus also known asMezzanine bus.

Computer 810 typically includes a variety of computer readable media.Computer readable media can be any available media that can be accessedby computer 810 and includes both volatile and nonvolatile media,removable and non-removable media. By way of example, and notlimitation, computer readable media may comprise computer storage mediaand communication media. Computer storage media includes both volatileand nonvolatile, removable and non-removable media implemented in anymethod or technology for storage of information such as computerreadable instructions, data structures, program modules or other data.Computer storage media include, but are not limited to, RAM, ROM,EEPROM, flash memory or other memory technology, CD-ROM, digitalversatile disks (DVD) or other optical disk storage, magnetic cassettes,magnetic tape, magnetic disk storage or other magnetic storage devices,or any other one or more media which may be used to store the desiredinformation and may be accessed by computer 810. Communication mediatypically embody computer readable instructions, data structures,program modules or other data in a modulated data signal such as acarrier wave or other transport mechanism and includes any informationdelivery media. The term “modulated data signal” means a signal that hasone or more of its characteristics set or changed in such a manner as toencode information in the signal. By way of example, and not limitation,communication media include wired media such as a wired network ordirect-wired connection, and wireless media such as acoustic, RF,infrared and other wireless media. Combinations of the any of the aboveshould also be included within the scope of computer readable media.

The system memory 830 includes computer storage media in the form ofvolatile and/or nonvolatile memory such as read only memory (ROM) 831and random access memory (RAM) 832. A basic input/output system 833(BIOS), containing the basic routines that help to transfer informationbetween elements within computer 810, such as during start-up, istypically stored in ROM 831. RAM 832 typically contains data and/orprogram modules that are immediately accessible to and/or presentlybeing operated on by processing unit 820. By ways of example, and notlimitation, FIG. 12 illustrates operating system 834, applicationprograms 835, other program modules 839 and program data 837.

The computer 810 may also include other removable/non-removable,volatile/nonvolatile computer storage media. By way of example only,FIG. 12 illustrates a hard disk drive 841 that reads from or writes tonon-removable, nonvolatile magnetic media, a magnetic disk drive 851that reads from or writes to a removable, nonvolatile magnetic disk 852,and an optical disk drive 855 that reads from or writes to a removable,nonvolatile optical disk 859 such as a CD ROM or other optical media.Other removable/non-removable, volatile/nonvolatile computer storagemedia that can be used in the exemplary computing system include, butare not limited to, magnetic tape cassettes, flash memory cards, digitalversatile disks, digital video tape, solid state RAM, solid state ROM,and the like. The hard disk drive 841 is typically connected to thesystem bus 821 through an non-removable memory interface such asinterface 840, and magnetic disk drive 851 and optical disk drive 855are typically connected to the system bus 821 by a removable memoryinterface, such as interface 850.

The drives and their associated computer storage media discussed aboveand illustrated in FIG. 12, provide storage of computer readableinstructions, data structures, program modules and other data for thecomputer 810. In FIG. 12, for example, hard disk drive 841 isillustrated as storing operating system 844, application programs 845,other program modules 849, and program data 847. Note that thesecomponents can either be the same as or different from operating system834, application programs 835, other program modules 539, and programdata 837. Operating system 844, application programs 845, other programmodules 849, and program data 847 are given different numbers here toillustrate that, at a minimum, they are different copies. A user mayenter commands and information into the computer 810 through inputdevices such as a keyboard 892 and pointing device 891, commonlyreferred to as a mouse, trackball or touch pad. Other input devices (notshown) may include a microphone, joystick, game pad, satellite dish,scanner, or the like. These and other input devices are often connectedto the processing unit 820 through a user input interface 590 that iscoupled to the system bus, but may be connected by other interface andbus structures, such as a parallel port, game port or a universal serialbus (USB). A monitor 891 or other type of display device is alsoconnected to the system bus 821 via an interface, such as a videointerface 890. In addition to the monitor, computers may also includeother peripheral output devices such as speakers 897 and printer 899,which may be connected through a output peripheral interface 895.

The computer 810 may operate in a networked environment using logicalconnections to one or more remote computers, such as a remote computer880. The remote computer 880 may be a personal computer, a server, arouter, a network PC, a peer device or other common network node, andtypically includes many or all of the elements described above relativeto the computer 810, although only a memory storage device 881 has beenillustrated in FIG. 12. The logical connections depicted in FIG. 12include a local area network (LAN) 871 and a wide area network (WAN)873, but may also include other networks. Such networking environmentsare commonplace in offices, enterprise-wide computer networks, intranetsand the Internet.

When used in a LAN networking environment, the computer 810 is connectedto the LAN 871 through a network interface or adapter 870. When used ina WAN networking environment, the computer 810 typically includes amodem 872 or other means for establishing communications over the WAN873, such as the Internet. The modem 872, which may be internal orexternal, may be connected to the system bus 821 via the user inputinterface 890, or other appropriate mechanism. In a networkedenvironment, program modules depicted relative to the computer 810, orportions thereof, may be stored in the remote memory storage device. Byway of example, and not limitation, FIG. 12 illustrates remoteapplication programs 885 as residing on memory device 881. It will beappreciated that the network connections shown are exemplary and othermeans of establishing a communications link between the computers may beused.

Embodiments of the invention may be embodied as a computer readablestorage medium (or multiple computer readable media) (e.g., a computermemory, one or more floppy discs, compact discs (CD), optical discs,digital video disks (DVD), magnetic tapes, flash memories, circuitconfigurations in Field Programmable Gate Arrays or other semiconductordevices, or other tangible computer storage medium) encoded with one ormore programs that, when executed on one or more computers or otherprocessors, perform methods that implement the various embodiments ofthe invention discussed above. As is apparent from the foregoingexamples, a computer readable storage medium may retain information fora sufficient time to provide computer-executable instructions in anon-transitory form. Such a computer readable storage medium or mediacan be transportable, such that the program or programs stored thereoncan be loaded onto one or more different computers or other processorsto implement various aspects of the present invention as discussedabove. As used herein, the term “computer-readable storage medium”encompasses only a tangible machine, mechanism or device from which acomputer may read information. Alternatively or additionally, theinvention may be embodied as a computer readable medium other than acomputer-readable storage medium. Examples of computer readable mediawhich are not computer readable storage media include transitory media,like propagating signals.

Having thus described several aspects of at least one embodiment of thisinvention, it is to be appreciated that various alterations,modifications, and improvements will readily occur to those skilled inthe art. Such alterations, modifications, and improvements are intendedto be part of this disclosure, and are intended to be within the spiritand scope of the invention. Further, though advantages of the presentinvention are indicated, it should be appreciated that not everyembodiment of the invention will include every described advantage. Someembodiments may not implement any features described as advantageousherein. Accordingly, the foregoing description and drawings are by wayof example only.

Various aspects of the present invention may be used alone, incombination, or in a variety of arrangements not specifically discussedin the embodiments described in the foregoing and is therefore notlimited in its application to the details and arrangement of componentsset forth in the foregoing description or illustrated in the drawings.For example, aspects described in one embodiment may be combined in anymanner with aspects described in other embodiments.

The invention may be embodied as a method, of which various exampleshave been described. The acts performed as part of the methods may beordered in any suitable way. Accordingly, embodiments may be constructedin which acts are performed in an order different than illustrated,which may include different (e.g., more or less) acts than those whichare described, and/or which may involve performing some actssimultaneously, even though the acts are shown as being performedsequentially in the embodiments specifically described above.

Use of ordinal terms such as “first,” “second,” “third,” etc., in theclaims to modify a claim element does not by itself connote anypriority, precedence, or order of one claim element over another or thetemporal order in which acts of a method are performed, but are usedmerely as labels to distinguish one claim element having a certain namefrom another element having a same name (but for use of the ordinalterm) to distinguish the claim elements.

Also, the phraseology and terminology used herein is for the purpose ofdescription and should not be regarded as limiting. The use of“including,” “comprising,” or “having,” “containing,” “involving,” andvariations thereof herein, is meant to encompass the items listedthereafter and equivalents thereof as well as additional items.

What is claimed is:
 1. A method, comprising acts of: (A) receivingacceleration data captured by a personal electronic device residingwithin an automobile during operation of the automobile; (B) receivinggeolocation data of the personal electronic device residing within theautomobile during the operation of the automobile; and (C) determiningat least one of a year, make, model, class and type of the automobilebased at least in part on the data received in the acts (A) and (B). 2.The method of claim 1, wherein the data received in the act (A)indicates acceleration of the personal electronic device in x-, y- andz-directions over time.
 3. The method of claim 1, wherein the datareceived in the act (A) indicates an orientation of the personalelectronic device at one or more points in time.
 4. The method of claim1, wherein the method comprises, prior to the act (A), an act oftraining at least one model to determine the at least one of the year,make, model, class and type of the automobile using other data capturedby at least one personal electronic device residing within at least onevehicle during operation of the at least one vehicle, and wherein theact (C) comprises applying the at least one model to the data receivedin the acts (A) and (B) in making the determination in the act (C). 5.The method of claim 4, wherein: the other data indicates acceleration ofthe personal electronic device in the time domain; and the act performedprior to the act (A) comprises computing, from the data indicatingacceleration of the personal electronic device in the time domain, oneor more power spectra in the frequency domain, and training the at leastone model using the one or more power spectra in the frequency domain.6. The method of claim 1, wherein the method comprises employing thedetermination in the act (C) to provide automobile-specific informationto a user associated with the personal electronic device or to anindividual other than user.
 7. The method of claim 1, wherein the methodcomprises receiving other information captured by the personalelectronic device relating to operation of the automobile.
 8. The methodof claim 7, wherein the other information relates to behavior of a userassociated with the personal electronic device during operation of theautomobile.
 9. The method of claim 1, wherein the act (C) is performedby the personal electronic device.
 10. The method of claim 1, whereinthe acts (A) and (B) are performed by a server in networkedcommunication with the personal electronic device.
 11. The method ofclaim 1, wherein the act (B) comprises receiving data of a globalpositioning system component of the personal electronic device.
 12. Atleast one computer-readable storage medium having instructions recordedthereon which, when executed by a computer, cause the computer toperform a method comprising acts of: (A) receiving acceleration datacaptured by a personal electronic device residing within an automobileduring operation of the automobile; (B) receiving geolocation data ofthe personal electronic device residing within the automobile during theoperation of the automobile; and (C) determining at least one of a year,make, model, class and type of the automobile based at least in part onthe data received in the acts (A) and (B).
 13. A computing system,comprising: at least one computer-readable storage device, storinginstructions; and at least one computer processor, programmed via theinstructions to: receive acceleration data captured by a personalelectronic device residing within an automobile during operation of theautomobile; receive geolocation data of the personal electronic deviceresiding within the automobile during the operation of the automobile;and determine at least one of a year, make, model, class and type of theautomobile based at least in part on the received acceleration data andgeolocation data.
 14. The computing system of claim 13, wherein thereceived acceleration data indicates acceleration of the personalelectronic device in x-, y- and z-directions over time.
 15. Thecomputing system of claim 13, wherein the received acceleration dataindicates an orientation of the personal electronic device at one ormore points in time.
 16. The computing system of claim 13, wherein theat least one computer processor is programmed to, prior to receiving theacceleration data, train at least one model to determine the at leastone of the year, make, model, class and type of the automobile usingother data captured by at least one personal electronic device residingwithin at least one vehicle during operation of the at least onevehicle, and to apply the at least one model to the receivedacceleration data and geolocation data in making the determination. 17.The computing system of claim 16, wherein the other data indicatesacceleration of the personal electronic device in the time domain, andwherein the at least one computer processor is programmed to, prior toreceiving the acceleration data, compute, from the data indicatingacceleration of the personal electronic device in the time domain, oneor more power spectra in the frequency domain, and train the at leastone model using the one or more power spectra in the frequency domain.18. The computing system of claim 13, wherein the at least one computerprocessor is programmed to employ the determination in providingautomobile-specific information to a user.
 19. The computing system ofclaim 18, wherein the user is associated with the personal electronicdevice.
 20. The computing system of claim 13, wherein the at least onecomputer processor is programmed to receive other information capturedby the personal electronic device relating to operation of theautomobile.
 21. The computing system of claim 20, wherein the otherinformation relates to behavior of a user associated with the personalelectronic device during operation of the automobile.
 22. The computingsystem of claim 13, wherein the at least one computer processor resideswithin the personal electronic device.